Predictive analytics has come a long way and, in an era defined by the ever-increasing influx of data and heightened customer demands, businesses no longer can deny its strategic importance.
Industries such as insurance, financial services, and retail have used predictive analytics for decades, while others are just getting started. So what’s new? Predictive analytics now is being used to support day-to-day business operations and decision making rather than only special, retrospective projects. Companies that use predictive analytics effectively can glean forward-looking insights that enable them to spot new business opportunities and innovate more quickly.
So why aren’t more companies doing it? Many simply don’t know where to start. Becoming a big data dynamo in your organization doesn’t require a complete rethink and change in how things are done. However, while business leaders agree on the importance of a data-driven approach to survive the next decade, an overwhelming number admit that, at the core, they still struggle with information overload and deriving actionable insights from data they already possess.
Using predictive analytics, while difficult, is possible and also increasingly necessary to compete effectively today. In fact, in a recent study by Capgemini, 65 percent of respondents agreed that their business runs the risk of becoming irrelevant if they do not embrace big data.
Here are a few tips to position your company for predictive analytics success:
With a sea of potentially useless data, narrow down your options and find the right area of your business to get started. CapGemini studies confirm that the number-one guiding principle to harnessing success with big data is to focus on solutions supporting your primary business objectives. Depending on your function, some considerations are market planning, account intelligence, and optimizing operations to streamline processes.
Some people believe those with the most data will win. My experience is that those with the right data win. The quality of data sources must be the top priority when launching an analytics function. Start small and look to incorporate both internal and external data sets.
It takes more than data scientists crunching data to be successful. Having the business perspective to develop actionable insights is essential. Build a diverse analytics team with a wide variety of skill sets, including data officers, analysts, engineers, scientists, and consultants.
As you build a “data bench,” there are three or four roles to consider. The first is a data analyst, who is intimately familiar with how to extract and transform data for its intended purpose. Second is the data engineer, the person who knows how the data is being captured, the servers where the data is located, and the tools that are required to extract data for analysis. Third are the data scientists, who can create a profile or do clustering analysis on data. Finally, segment experts or consultants can contextualize the findings and deliver recommendations that are compelling to senior leaders.
Incorporating analytics into the decision-making process is not always welcomed by all stakeholders, so it’s important to manage the change strategically. You can ensure a smoother transition through the following steps:
John Smits is Chief Data Officer for EMC Corporation’s Global Business Operations. John leads EMC’s Business Insights and Analytics team in the development of market segmentation, customer intelligence, and business performance analytics that are enabling its digital sales transformation with big data insights. Check out John’s Wise Practitioner – Predictive Analytics Interview Series.